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From |
William Buchanan <william@williambuchanan.net> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Regression discontinuity with interrupted time series |

Date |
Thu, 7 Mar 2013 12:46:34 -0800 |

I have tried to find the paper that Joshua referenced in the initial query and had little luck. I found another paper written by one of those authors citing the paper referenced by Joshua. Perhaps it would be helpful if Joshua could provide a link to find the paper that he referenced so others can get a better idea of what he is trying to do? On Mar 7, 2013, at 11:20 AM, Ariel Linden. DrPH <ariel.linden@gmail.com> wrote: > Austin is absolutely right here. For an interesting historical perspective, > RD and ITSA originated from the same basic idea. That is, find a continuous > x-variable and define a cutoff. In the case of ITSA, the x-variable is > "time" and we are interested in both the "step" (first month of the > intervention), and the "trend" (of all the months after the start of the > intervention). In RD, the x-variable is also continuous, but here we care > only about the average effect determined by the local values on either side > of the cutoff. > > Given this general perspective, it seems an oddity to combine the two > methods, since they require a x-variable which is going to differ, and a > cutoff, which is going to differ. It seems as if you'd be referring to a > sub-group analysis in which the subgroupings were decided at one level of > the analysis and then compared at the next level (e.g., right side of the > cutoff on the RD analysis, then analyzed at the time cutoff on the ITSA)... > > It is difficult to imagine how this could be done in any valid fashion... > > Ariel > > Date: Wed, 6 Mar 2013 16:51:24 -0500 > From: Austin Nichols <austinnichols@gmail.com> > Subject: Re: st: Regression discontinuity with interrupted time series > > Joshua Mitts <joshua.mitts@yale.edu>: > You need to be a lot more clear about your scientific model of the > data generating process. I have not read the cited paper, but I am > doubtful about the marriage of RD and ITS. The point of RD is that > outcomes of observations on either side of the cutoff are identical on > average except for treatment status so the jump in outcomes at the > cutoff is the effect of treatment; that is not true if you think > treatment has some dynamic impacts, or in other words the effect of > treatment increases (or decreases) in the assigment variable, so that > you do not want the instantaneous impact of treatment at the cutoff, > but some effect away from the cutoff. Imagine it this way: you have > an announcement event that affects stock prices at noon but you do not > want to compare stock prices at one minute after noon to noon because > you think the event actually changes the time path of investment and > you want changes in market valuation over some period until the > announced policy change takes place. This is no longer a good > situation for RD if you are thinking about comparing across time. You > can still use the dummy for above the cutoff at time t as a dummy for > treatment in periods after t, but the comparison will not have the > clean RD interpretation (where the counterfactual is essentially > observed if the data is dense around the cutoff) if you are using time > as an assignment variable. Can you assume linear trends before and > after time t? You can define time as time minus t so that the constant > is the jump in mean outcomes at t, and the dummy for above the cutoff > is the instrument for treatment; if they are perfectly collinear you > have a "sharp" design but you may want to also estimate a change in > trend after t for the treatment group, for which you may need an > additional instrument--the key here is how the cutoff is defined. Is > the variable that is compared to the cutoff subject to manipulation? > Changing over time? Only examined at time t? If you have an assigment > variable that is not time, you are back in the world of RD, and you > may be better off with a long difference in outcomes (reducing > problems due to measurement error in fixed effect models), e.g. y at > t+5 minus y at t-5, regressed on treatment in the usual RD manner. > > On Wed, Mar 6, 2013 at 11:00 AM, Joshua Mitts <joshua.mitts@yale.edu> wrote: >> Hi, >> >> How can I combine regression discontinuity with interrupted time >> series analysis in Stata? I have repeated observations of an outcome >> variable for ~180 units over time, an intervention at time t at a >> cutoff value, and more repeated observations post-intervention. With >> ordinary RD, I can only measure the outcome individually at t+1, t+2, >> etc. It seems this is an active area of research[1], but I'm not sure >> how to implement it in Stata. Any suggestions would be greatly >> appreciated. >> >> Thank you, >> Josh >> >> -- >> Joshua Mitts >> Yale Law School '13 >> joshua.mitts@yale.edu > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**re: Re: st: Regression discontinuity with interrupted time series***From:*"Ariel Linden. DrPH" <ariel.linden@gmail.com>

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